2005/10/31 by Bhuvnesh Jain, Mike Jarvis, Gary Bernstein · 31 citations
Physics and Astronomy · #Anisotropy #Astronomy and Astrophysical Research #Cosmology #Galaxies: Formation, Evolution, Phenomena #Galaxy #Point spread function #Principal component analysis #Radio Astronomy Observations and Technology #Residual #Systematic error #Weak gravitational lensing #astro-ph
paper · pdf · doi:10.1088/1475-7516/2006/02/001
published in Journal of Cosmology and Astroparticle Physics 2006(02), 001 (Institute of Physics) · 18 pages, 1 figure; revised submission with minor edits
arxiv created 2005/12/23 · openalex publication_date 2006/02/02 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
Given the basic parameters of a cosmic shear weak lensing survey, how well can systematic errors due to anisotropy in the point spread function (PSF) be corrected? The largest source of error in this correction to date has been the interpolation of the PSF to the locations of the galaxies. To address this error, we separate the PSF patterns into components that recur in multiple exposures/pointings and those that vary randomly between different exposures (such as those due to the atmosphere). In an earlier study we developed a principal component approach to correct the recurring PSF patterns. In this paper we show how randomly varying PSF patterns can also be circumvented in the measurement of shear correlations. For the two-point correlation function this is done by simply using pairs of galaxy shapes measured in different exposures. Combining the two techniques allows us to tackle generic combinations of PSF anisotropy patterns. The second goal of this paper is to give a formalism for quantifying residual systematic errors due to PSF patterns. We show how the main PSF corrections improve with increasing survey area (and thus can stay below the reduced statistical errors), and we identify the residual errors which do not scale with survey area. Our formalism can be applied both to planned lensing surveys to optimize survey strategy and to actual lensing data to quantify residual errors.